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Chapter 1. Overview of the model customization workflow

Red Hat AI model customization empowers you to tailor artificial intelligence models to your unique data and operational requirements. The model customization process involves the training or fine-tuning of pre-existing models with proprietary datasets, followed by their deployment with specific configurations on the Red Hat OpenShift AI platform. This comprehensive approach is facilitated by a powerful suite of integrated toolkits that streamline and accelerate the development of generative AI applications.

The workflow for customizing models includes the following tasks:

Set up your working environment
Ensure reliable and secure access to supported libraries with the Red Hat Hosted Python index. For details, see Set up your working environment.
Prepare your data for AI consumption

To prepare your data, use Docling, a powerful Python library to transform unstructured data (such as text documents, images, and audio files) into structured formats that models can consume. For details, see Prepare your data for AI consumption.

To automate data processing tasks, you can build Kubeflow Pipelines (KFP), see Automate data processing steps by building AI pipelines.

Generate synthetic data
Use the Red Hat AI Synthetic Data Generation (SDG) Hub framework to build, compose, and scale synthetic data pipelines with modular blocks. With the SDG Hub, you can extend your synthetic data pipelines with custom blocks to fit your domain, replace ad hoc scripts with the SDG Hub repeatable framework, and scale data generation with asynchronous execution and monitoring. For details, see Generate synthetic data.
Train a model by using your prepared data

After you prepare your data, use the Red Hat AI Training Hub to simplify and accelerate the process of fine-tuning and customizing a foundation model by using your own data.

You can extend a base notebook to use distributed training across multiple nodes by using the KubeFlow Trainer Operator (KFTO). The KFTO, abstracts the underlying infrastructure complexity of distributed training and fine-tuning of models. The iterative process of fine-tuning significantly reduces the time and resources required compared to training models from scratch.

For details, see Train a model by using your prepared data.

Serve and consume a customized model

After you customize a model, you can serve your customized models as APIs (Application Programming Interfaces). Serving a model as an API enables seamless integration into existing or newly developed applications.

Learn more about serving and consuming a customized model Deploying models on the model serving platform.

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